multimodalart's picture
Cosmetic changes
f1d9efe
raw
history blame
20.7 kB
import gradio as gr
import os
from pathlib import Path
import argparse
import shutil
from train_dreambooth import run_training
from convertosd import convert
from PIL import Image
from slugify import slugify
import requests
import torch
import zipfile
import urllib.parse
import gc
from diffusers import StableDiffusionPipeline
from huggingface_hub import snapshot_download
css = '''
.instruction{position: absolute; top: 0;right: 0;margin-top: 0px !important}
.arrow{position: absolute;top: 0;right: -110px;margin-top: -8px !important}
#component-4, #component-3, #component-10{min-height: 0}
.duplicate-button img{margin: 0}
'''
maximum_concepts = 3
#Pre download the files even if we don't use it here
model_to_load = snapshot_download(repo_id="multimodalart/sd-fine-tunable")
safety_checker = snapshot_download(repo_id="multimodalart/sd-sc")
def zipdir(path, ziph):
# ziph is zipfile handle
for root, dirs, files in os.walk(path):
for file in files:
ziph.write(os.path.join(root, file),
os.path.relpath(os.path.join(root, file),
os.path.join(path, '..')))
def swap_text(option):
mandatory_liability = "You must have the right to do so and you are liable for the images you use, example:"
if(option == "object"):
instance_prompt_example = "cttoy"
freeze_for = 50
return [f"You are going to train `object`(s), upload 5-10 images of each object you are planning on training on from different angles/perspectives. {mandatory_liability}:", '''<img src="file/cat-toy.png" />''', f"You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to 512x512.", freeze_for]
elif(option == "person"):
instance_prompt_example = "julcto"
freeze_for = 100
return [f"You are going to train a `person`(s), upload 10-20 images of each person you are planning on training on from different angles/perspectives. {mandatory_liability}:", '''<img src="file/person.png" />''', f"You should name the files with a unique word that represent your concept (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to 512x512.", freeze_for]
elif(option == "style"):
instance_prompt_example = "trsldamrl"
freeze_for = 10
return [f"You are going to train a `style`, upload 10-20 images of the style you are planning on training on. Name the files with the words you would like {mandatory_liability}:", '''<img src="file/trsl_style.png" />''', f"You should name your files with a unique word that represent your concept (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to 512x512.", freeze_for]
def count_files(*inputs):
file_counter = 0
concept_counter = 0
for i, input in enumerate(inputs):
if(i < maximum_concepts-1):
files = inputs[i]
if(files):
concept_counter+=1
file_counter+=len(files)
uses_custom = inputs[-1]
type_of_thing = inputs[-4]
if(uses_custom):
Training_Steps = int(inputs[-3])
else:
if(type_of_thing == "person"):
Training_Steps = file_counter*200*2
else:
Training_Steps = file_counter*200
return(gr.update(visible=True, value=f"You are going to train {concept_counter} {type_of_thing}(s), with {file_counter} images for {Training_Steps} steps. This should take around {round(Training_Steps/1.5, 2)} seconds, or {round((Training_Steps/1.5)/3600, 2)} hours. As a reminder, the T4 GPU costs US$0.60 for 1h. Once training is over, don't forget to swap the hardware back to CPU."))
def train(*inputs):
torch.cuda.empty_cache()
if 'pipe' in globals():
del pipe
gc.collect()
if "IS_SHARED_UI" in os.environ:
raise gr.Error("This Space only works in duplicated instances")
if os.path.exists("output_model"): shutil.rmtree('output_model')
if os.path.exists("instance_images"): shutil.rmtree('instance_images')
if os.path.exists("diffusers_model.zip"): os.remove("diffusers_model.zip")
if os.path.exists("model.ckpt"): os.remove("model.ckpt")
file_counter = 0
for i, input in enumerate(inputs):
if(i < maximum_concepts-1):
if(input):
os.makedirs('instance_images',exist_ok=True)
files = inputs[i+(maximum_concepts*2)]
prompt = inputs[i+maximum_concepts]
if(prompt == "" or prompt == None):
raise gr.Error("You forgot to define your concept prompt")
for j, file_temp in enumerate(files):
file = Image.open(file_temp.name)
width, height = file.size
side_length = min(width, height)
left = (width - side_length)/2
top = (height - side_length)/2
right = (width + side_length)/2
bottom = (height + side_length)/2
image = file.crop((left, top, right, bottom))
image = image.resize((512, 512))
extension = file_temp.name.split(".")[1]
image = image.convert('RGB')
image.save(f'instance_images/{prompt}_({j+1}).jpg', format="JPEG", quality = 100)
file_counter += 1
os.makedirs('output_model',exist_ok=True)
uses_custom = inputs[-1]
type_of_thing = inputs[-4]
if(uses_custom):
Training_Steps = int(inputs[-3])
Train_text_encoder_for = int(inputs[-2])
else:
Training_Steps = file_counter*200
if(type_of_thing == "object"):
Train_text_encoder_for=30
elif(type_of_thing == "person"):
Train_text_encoder_for=60
elif(type_of_thing == "style"):
Train_text_encoder_for=15
class_data_dir = None
stptxt = int((Training_Steps*Train_text_encoder_for)/100)
args_general = argparse.Namespace(
image_captions_filename = True,
train_text_encoder = True,
stop_text_encoder_training = stptxt,
save_n_steps = 0,
pretrained_model_name_or_path = model_to_load,
instance_data_dir="instance_images",
class_data_dir=class_data_dir,
output_dir="output_model",
instance_prompt="",
seed=42,
resolution=512,
mixed_precision="fp16",
train_batch_size=1,
gradient_accumulation_steps=1,
use_8bit_adam=True,
learning_rate=2e-6,
lr_scheduler="polynomial",
lr_warmup_steps = 0,
max_train_steps=Training_Steps,
)
print("Starting training...")
run_training(args_general)
gc.collect()
torch.cuda.empty_cache()
print("Adding Safety Checker to the model...")
shutil.copytree(f"{safety_checker}/feature_extractor", "output_model/feature_extractor")
shutil.copytree(f"{safety_checker}/safety_checker", "output_model/safety_checker")
shutil.copy(f"model_index.json", "output_model/model_index.json")
print("Zipping model file...")
with zipfile.ZipFile('diffusers_model.zip', 'w', zipfile.ZIP_DEFLATED) as zipf:
zipdir('output_model/', zipf)
print("Training completed!")
return [gr.update(visible=True, value=["diffusers_model.zip"]), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)]
def generate(prompt):
torch.cuda.empty_cache()
from diffusers import StableDiffusionPipeline
global pipe
pipe = StableDiffusionPipeline.from_pretrained("./output_model", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
image = pipe(prompt).images[0]
return(image)
def push(model_name, where_to_upload, hf_token):
if(not os.path.exists("model.ckpt")):
convert("output_model", "model.ckpt")
from huggingface_hub import HfApi, HfFolder, CommitOperationAdd
from huggingface_hub import create_repo
model_name_slug = slugify(model_name)
api = HfApi()
your_username = api.whoami(token=hf_token)["name"]
if(where_to_upload == "My personal profile"):
model_id = f"{your_username}/{model_name_slug}"
else:
model_id = f"sd-dreambooth-library/{model_name_slug}"
headers = {"Authorization" : f"Bearer: {hf_token}", "Content-Type": "application/json"}
response = requests.post("https://huggingface.co/organizations/sd-dreambooth-library/share/SSeOwppVCscfTEzFGQaqpfcjukVeNrKNHX", headers=headers)
images_upload = os.listdir("instance_images")
image_string = ""
instance_prompt_list = []
previous_instance_prompt = ''
for i, image in enumerate(images_upload):
instance_prompt = image.split("_")[0]
if(instance_prompt != previous_instance_prompt):
title_instance_prompt_string = instance_prompt
instance_prompt_list.append(instance_prompt)
else:
title_instance_prompt_string = ''
previous_instance_prompt = instance_prompt
image_string = f'''{title_instance_prompt_string} (use that on your prompt)
{image_string}![{instance_prompt} {i}](https://huggingface.co/{model_id}/resolve/main/concept_images/{urllib.parse.quote(image)})'''
readme_text = f'''---
license: creativeml-openrail-m
tags:
- text-to-image
---
### {model_name} Dreambooth model trained by {api.whoami(token=hf_token)["name"]} with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training)
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
{image_string}
'''
#Save the readme to a file
readme_file = open("model.README.md", "w")
readme_file.write(readme_text)
readme_file.close()
#Save the token identifier to a file
text_file = open("token_identifier.txt", "w")
text_file.write(', '.join(instance_prompt_list))
text_file.close()
create_repo(model_id,private=True, token=hf_token)
operations = [
CommitOperationAdd(path_in_repo="token_identifier.txt", path_or_fileobj="token_identifier.txt"),
CommitOperationAdd(path_in_repo="README.md", path_or_fileobj="model.README.md"),
CommitOperationAdd(path_in_repo=f"model.ckpt",path_or_fileobj="model.ckpt")
]
api.create_commit(
repo_id=model_id,
operations=operations,
commit_message=f"Upload the model {model_name}",
token=hf_token
)
api.upload_folder(
folder_path="output_model",
repo_id=model_id,
token=hf_token
)
api.upload_folder(
folder_path="instance_images",
path_in_repo="concept_images",
repo_id=model_id,
token=hf_token
)
return [gr.update(visible=True, value=f"Successfully uploaded your model. Access it [here](https://huggingface.co/{model_id})"), gr.update(visible=True, value=["diffusers_model.zip", "model.ckpt"])]
def convert_to_ckpt():
convert("output_model", "model.ckpt")
return gr.update(visible=True, value=["diffusers_model.zip", "model.ckpt"])
with gr.Blocks(css=css) as demo:
with gr.Box():
if "IS_SHARED_UI" in os.environ:
gr.HTML(f'''
<div class="gr-prose" style="max-width: 80%">
<h2>Attention - This Space doesn't work in this shared UI</h2>
<p>For it to work, you have to duplicate the Space and run it on your own profile using a (paid) private T4 GPU for training. As each T4 costs US$0.60/h, it should cost < US$1 to train a model with less than 100 images using default settings!&nbsp;&nbsp;<a class="duplicate-button" style="display:inline-block" href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></p>
<img class="instruction" src="file/duplicate.png">
<img class="arrow" src="file/arrow.png" />
</div>
''')
else:
gr.HTML(f'''
<div class="gr-prose" style="max-width: 80%">
<h2>You have successfully duplicated the Dreambooth Training Space</h2>
<p>If you haven't already, <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}/settings">attribute a T4 GPU to it (via the Settings tab)</a> and run the training below. You will be billed by the minute from when you activate the GPU until when you turn it off.</p>
</div>
''')
gr.Markdown("# Dreambooth training")
gr.Markdown("Customize Stable Diffusion by giving it a few examples. You can train up to three concepts by providing examples for each. This Space is based on TheLastBen's [fast-DreamBooth Colab](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) with 🧨 diffusers")
with gr.Row():
type_of_thing = gr.Dropdown(label="What would you like to train?", choices=["object", "person", "style"], value="object", interactive=True)
with gr.Row():
with gr.Column():
thing_description = gr.Markdown("You are going to train an `object`, please upload 5-10 images of the object you are planning on training on from different angles/perspectives. You must have the right to do so and you are liable for the images you use, example:")
thing_image_example = gr.HTML('''<img src="file/cat-toy.png" />''')
things_naming = gr.Markdown("You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `cttoy` here). Images will be automatically cropped to 512x512.")
with gr.Column():
file_collection = []
concept_collection = []
buttons_collection = []
delete_collection = []
is_visible = []
row = [None] * maximum_concepts
for x in range(maximum_concepts):
ordinal = lambda n: "%d%s" % (n, "tsnrhtdd"[(n // 10 % 10 != 1) * (n % 10 < 4) * n % 10::4])
if(x == 0):
visible = True
is_visible.append(gr.State(value=True))
else:
visible = False
is_visible.append(gr.State(value=False))
file_collection.append(gr.File(label=f'''Upload the images for your {ordinal(x+1) if (x>0) else ""} concept''', file_count="multiple", interactive=True, visible=visible))
with gr.Column(visible=visible) as row[x]:
concept_collection.append(gr.Textbox(label=f'''{ordinal(x+1) if (x>0) else ""} concept prompt - use a unique, made up word to avoid collisions'''))
with gr.Row():
if(x < maximum_concepts-1):
buttons_collection.append(gr.Button(value="Add +1 concept", visible=visible))
if(x > 0):
delete_collection.append(gr.Button(value=f"Delete {ordinal(x+1)} concept"))
counter_add = 1
for button in buttons_collection:
if(counter_add < len(buttons_collection)):
button.click(lambda:
[gr.update(visible=True),gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), True, None],
None,
[row[counter_add], file_collection[counter_add], buttons_collection[counter_add-1], buttons_collection[counter_add], is_visible[counter_add], file_collection[counter_add]], queue=False)
else:
button.click(lambda:[gr.update(visible=True),gr.update(visible=True), gr.update(visible=False), True], None, [row[counter_add], file_collection[counter_add], buttons_collection[counter_add-1], is_visible[counter_add]], queue=False)
counter_add += 1
counter_delete = 1
for delete_button in delete_collection:
if(counter_delete < len(delete_collection)+1):
delete_button.click(lambda:[gr.update(visible=False),gr.update(visible=False), gr.update(visible=True), False], None, [file_collection[counter_delete], row[counter_delete], buttons_collection[counter_delete-1], is_visible[counter_delete]], queue=False)
counter_delete += 1
with gr.Accordion("Custom Settings", open=False):
swap_auto_calculated = gr.Checkbox(label="Use custom settings")
gr.Markdown("If not checked, the number of steps and % of frozen encoder will be tuned automatically according to the amount of images you upload and whether you are training an `object`, `person` or `style` as follows: The number of steps is calculated by number of images uploaded multiplied by 20. The text-encoder is frozen after 10% of the steps for a style, 30% of the steps for an object and is fully trained for persons.")
steps = gr.Number(label="How many steps", value=800)
perc_txt_encoder = gr.Number(label="Percentage of the training steps the text-encoder should be trained as well", value=30)
type_of_thing.change(fn=swap_text, inputs=[type_of_thing], outputs=[thing_description, thing_image_example, things_naming, perc_txt_encoder], queue=False)
training_summary = gr.Textbox("", visible=False, label="Training Summary")
steps.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary], queue=False)
perc_txt_encoder.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary], queue=False)
for file in file_collection:
file.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary], queue=False)
train_btn = gr.Button("Start Training")
completed_training = gr.Markdown("# ✅ Training completed", visible=False)
with gr.Row():
with gr.Box(visible=False) as try_your_model:
gr.Markdown("## Try your model")
prompt = gr.Textbox(label="Type your prompt")
result_image = gr.Image()
generate_button = gr.Button("Generate Image")
with gr.Box(visible=False) as push_to_hub:
gr.Markdown("## Push to Hugging Face Hub")
model_name = gr.Textbox(label="Name of your model", placeholder="Tarsila do Amaral Style")
where_to_upload = gr.Dropdown(["My personal profile", "Public Library"], label="Upload to")
gr.Markdown("[A Hugging Face write access token](https://huggingface.co/settings/tokens), go to \"New token\" -> Role : Write. A regular read token won't work here.")
hf_token = gr.Textbox(label="Hugging Face Write Token")
push_button = gr.Button("Push to the Hub")
result = gr.File(label="Download the uploaded models in the diffusers format", visible=True)
success_message_upload = gr.Markdown(visible=False)
convert_button = gr.Button("Convert to CKPT", visible=False)
train_btn.click(fn=train, inputs=is_visible+concept_collection+file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[result, try_your_model, push_to_hub, convert_button, completed_training])
generate_button.click(fn=generate, inputs=prompt, outputs=result_image)
push_button.click(fn=push, inputs=[model_name, where_to_upload, hf_token], outputs=[success_message_upload, result])
convert_button.click(fn=convert_to_ckpt, inputs=[], outputs=result)
demo.launch(debug=True)